LID SPN TRAP

1460462_at

Med18 mediator of RNA polymerase II transcription, subunit 18 homolog (yeast)

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.011 0.919 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.78
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000138
Time: 22:05:59 Log-Likelihood: -101.02
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 63.7605 70.741 0.901 0.379 -84.302 211.823
C(dose)[T.1] 32.2441 86.992 0.371 0.715 -149.833 214.321
expression -1.5214 11.224 -0.136 0.894 -25.013 21.970
expression:C(dose)[T.1] 3.4913 14.122 0.247 0.807 -26.066 33.049
Omnibus: 0.312 Durbin-Watson: 1.875
Prob(Omnibus): 0.855 Jarque-Bera (JB): 0.480
Skew: 0.073 Prob(JB): 0.787
Kurtosis: 2.307 Cond. No. 167.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.51
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.82e-05
Time: 22:05:59 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.9145 42.190 1.183 0.251 -38.093 137.922
C(dose)[T.1] 53.6243 9.202 5.828 0.000 34.430 72.818
expression 0.6839 6.650 0.103 0.919 -13.188 14.556
Omnibus: 0.310 Durbin-Watson: 1.918
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.478
Skew: 0.057 Prob(JB): 0.787
Kurtosis: 2.303 Cond. No. 60.8

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 22:05:59 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.054
Model: OLS Adj. R-squared: 0.009
Method: Least Squares F-statistic: 1.190
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.288
Time: 22:05:59 Log-Likelihood: -112.47
No. Observations: 23 AIC: 228.9
Df Residuals: 21 BIC: 231.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 147.0485 62.130 2.367 0.028 17.843 276.254
expression -11.0785 10.157 -1.091 0.288 -32.201 10.045
Omnibus: 3.771 Durbin-Watson: 2.278
Prob(Omnibus): 0.152 Jarque-Bera (JB): 1.723
Skew: 0.326 Prob(JB): 0.422
Kurtosis: 1.828 Cond. No. 55.6

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.058 0.813 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.302
Method: Least Squares F-statistic: 3.018
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0758
Time: 22:05:59 Log-Likelihood: -70.797
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 37.2163 227.525 0.164 0.873 -463.563 537.996
C(dose)[T.1] 50.3129 274.645 0.183 0.858 -554.176 654.802
expression 5.8381 43.905 0.133 0.897 -90.797 102.473
expression:C(dose)[T.1] -0.5169 52.105 -0.010 0.992 -115.199 114.165
Omnibus: 2.340 Durbin-Watson: 0.809
Prob(Omnibus): 0.310 Jarque-Bera (JB): 1.750
Skew: -0.784 Prob(JB): 0.417
Kurtosis: 2.416 Cond. No. 272.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.938
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0272
Time: 22:05:59 Log-Likelihood: -70.797
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 39.1156 117.701 0.332 0.745 -217.333 295.564
C(dose)[T.1] 47.5941 17.044 2.792 0.016 10.459 84.729
expression 5.4711 22.636 0.242 0.813 -43.849 54.791
Omnibus: 2.318 Durbin-Watson: 0.808
Prob(Omnibus): 0.314 Jarque-Bera (JB): 1.739
Skew: -0.780 Prob(JB): 0.419
Kurtosis: 2.409 Cond. No. 83.7

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 22:05:59 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.095
Model: OLS Adj. R-squared: 0.025
Method: Least Squares F-statistic: 1.364
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.264
Time: 22:05:59 Log-Likelihood: -74.552
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -66.5791 137.536 -0.484 0.636 -363.707 230.549
expression 30.0582 25.735 1.168 0.264 -25.538 85.654
Omnibus: 0.073 Durbin-Watson: 1.465
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.246
Skew: 0.129 Prob(JB): 0.884
Kurtosis: 2.428 Cond. No. 78.7